Stephen Wolfram plays the role of Salonnière in this new, ongoing series of intellectual explorations with special guests.
Explanations for the last video [Difference Between Clinical and Evidence-Based Knowledge]: Why the statistical properties of a group will NOT particularize to any individual member of the group.
1) The law of large numbers (properties of aggregates) works in one direction. Why you can generalize from particulars, never particularize from generals.
2) The difference between clinical, statistical, and risk management approached. Why they don’t scale.
3) Never compare Mediocristan to Extremistan (Covid to car accidents).
Explained in 4 Minutes
Yedidya Levy briefly discusses some of the reasons Nassim Taleb states that Bitcoin will in fact go to Zero. Nassim recently published a paper titled “Bitcoin, Currencies, and Bubbles.” It is also referred to as #bitcoinblackpaper.
Link to #bitcoinblackpaper – fooledbyrandomness.com/BTC-QF.pdf
Link to supplementary material – fooledbyrandomness.com/BTC-QF-appendix.pdf
- Why violence did not drop (the Pinker Problem)?
- What an “infinite mean” means?
- What causes power laws?
LONDON, May 27, 2021 /PRNewswire/ — CoinGeek Conference is created to foster enterprise blockchain adoption and support technology to enable a new data ecosystem. Doing something that has never been done before requires opening doors to experts with many differing viewpoints, past conferences brought in the likes of Wikipedia Founder Jimmy Wales who had previously expressed the opinion that he would never allow Bitcoin to be used on his platform.
With the goal of hearing diverse opinions that spawn meaningful discussions, CoinGeek Zurich (June 8-10) can now confirm that both Nouriel Roubini and Nassim Nicholas Taleb will address those assembled on and offline with their thoughts, on where the value should come from in Blockchain and Digital Currencies.
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Power laws, extremely simplified.
We saw that 1) many metrics are stochastic, 2) what is stochastic can be hacked. This is the simplification of my work showing that “p-values are not p-values”, i.e. highly sample dependent, with a skewed distribution. For instance, for a “true” P value of .11, 53% of observations will show less than .05. This allows for hacking: in a few trials, a researcher can get a fake p-value of .01.
Paper is here and in Chapter 19 of SCOFT (Statistical Conseq of Fat Tails): Link to paper – A Short Note on P-Value Hacking